Example Concept: Model accountability can be achieved through the use of audit trails that record AI decision-making processes, allowing for post-hoc analysis and human intervention when necessary. Explainable AI techniques, like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations), provide insights into how AI models make decisions, ensuring that human operators can understand and verify the rationale behind AI actions.